1. Field of the Invention
The present invention relates to a pattern recognition system for recognizing various recognition targets such as speech data, character data, image data, etc.
2. Description of the Background Art
In the field of the pattern recognition aiming at the recognition of various recognition targets such as speech data, character data, image data, etc., the most typical conventionally known scheme has been a pattern matching scheme in which a feature vector is generated from an input pattern, and some kind of discrimination of the generated feature vector is attempted in a vector space containing the generated feature vectors, assuming that the distribution of the feature vectors in such a vector space is the Gaussian distribution.
However, in general, the distribution of the feature vectors in the vector space is not necessarily limited to the Gaussian distribution in practice, and it is actually much more frequent to encounter the non-Gaussian feature vector distributions. In such cases of the non-Gaussian feature vector distribution, it has been difficult to improve the recognition rate by the conventional pattern matching scheme based on the assumption of the Gaussian feature vector distribution.
On the other hand, there are many recent propositions for the other recognition schemes such as the neural network scheme and various nonlinear recognition schemes. However, none of these recent propositions has been really capable of resolving the above noted problem as none of these recent propositions introduces an accurate model for the actual feature vector distribution.
For example, in the neural network scheme, it has been claimed that the recognition system appropriate for the actual distribution is going to be constructed automatically, but in reality, it has been impossible to know what kind of model is used at what level of approximation with respect to the actual distribution, and it has also been impossible to take care of this situation at the system designing stage.
For instance, in a case of having a feature vector distribution in which two distribution blocks for two different categories are arranged in such a complicated manner that they have the almost identical center of gravity, it is impossible to separate these two distributions blocks linearly, i.e., by a straight line.
In addition, since such a feature vector distribution is apparently not Gaussian, it is difficult to recognize the patterns belonging to these categories by using any scheme based on the assumption of the Gaussian feature vector distribution. On the other hand, when the neural network scheme is used in this case, it cannot be ascertained exactly what is going to be recognized and how, so that there has been no guarantee for the neural network to be able to make an accurate recognition. In practice, the neural network may be able to distinguish a relatively simple distribution, but there is no guarantee that the same is true for much more complicated distributions.
Moreover, in the conventional pattern recognition scheme, the actual feature vector distribution has rarely been taken into consideration at the dictionary preparation stage or the dictionary modification stage, and some ideal Gaussian distribution has been assumed unwarrantedly, so that an effective and efficient dictionary preparation and modification has not been realized.